Investors should pivot from basic AI chatbots toward "Agentic" systems and infrastructure that connects models to enterprise data to eliminate the "context gap." Focus on platforms like OutSystems that orchestrate autonomous AI agents, as these are positioned to capture the most transformative market share. Maintain exposure to cloud infrastructure leaders like Amazon (AMZN) through partners like Mission Cloud that specialize in turning high AI spending into measurable ROI. Be cautious of "tool sprawl" and instead favor companies that consolidate AI workflows into a single interface to reduce employee burnout and the "toggle tax." For long-term growth, prioritize organizations that use AI to automate coordination tasks while reinvesting human capital into high-value skill acquisition and coaching.

Glean & WorkAI Institute (Private Research)

The podcast discusses the "Work AI Index for 2026" report, which identifies a phenomenon called "Bot-sitting"—the hidden human labor required to make AI usable in a professional environment.

• The Productivity Paradox: While 87% of digital workers use AI and save an average of 11 hours per week, only 13% of organizations report significant performance improvements.
• The "Bot-sitting" Drain: Workers spend an average of 6.4 hours per week managing AI. This includes:
  • Feeding Context: Providing the AI with necessary background info (2 hours/week).
  • Supervising Outputs: Verifying high-stakes answers (2 hours/week).
  • Debugging: Fixing mistakes or rerunning prompts (1 hour/week).
• The "Bot-sh*tting" Risk: High levels of "bot-sitting" lead to fatigue, causing workers to offload judgment entirely to the AI, resulting in unverified, low-quality work being moved upstream in the organization.
• Tool Sprawl: Using multiple AI tools increases the "toggle tax," making workers 35% more likely to report frequent exhaustion.

Takeaways

• Investment Theme: Look for companies solving the "context gap." The report suggests that the next wave of value isn't just in the LLM (Large Language Model) itself, but in the infrastructure that connects AI to enterprise data to reduce manual "bot-sitting."
• Efficiency vs. Opportunity: Investors should distinguish between "Efficiency AI" (doing old tasks faster) and "Opportunity AI" (agentic systems that change how work is done).
• Organizational Health: Companies that provide transparent AI usage data and formal rewards for AI skills (84% in high-performing orgs) are more likely to see a return on their AI investment than those just buying licenses.

Agentic Systems & AI Platforms

The discussion highlights a shift from "non-agentic" (chatbots) to "agentic" work (AI that can execute goals autonomously).

• OutSystems: Mentioned as a leading platform for building and governing agentic systems in the enterprise.
• Mission Cloud: An AWS partner focused on turning AI spend into measurable ROI by fixing deployments that "go sideways."
• KPMG: Highlighted for research on "sophisticated AI collaboration," suggesting that the highest impact users treat AI as a reasoning partner rather than just a search engine or ghostwriter.

Takeaways

• Sector Focus: The "Agentic" shift is the primary investment opportunity. Platforms that allow for the orchestration and governance of AI agents (like OutSystems) are positioned to capture the 13% of "transformative" enterprise market share.
• Cloud Infrastructure: Partners like Mission Cloud (CDW company) and AWS remain essential as enterprises struggle to prove ROI on the average $11.5 million spent on AI annually.

Major AI Models (ChatGPT, Claude, Gemini, Copilot)

The report correlates the "smartness" of a tool with the "sloppiness" of the user.

• ChatGPT (OpenAI) & Claude (Anthropic): Reported the highest productivity gains (67% and 59% respectively) but also the highest rates of "bot-sh*tting" (71% and 92% of users admitting to it monthly).
• The "Smarter Tool" Trap: As models become more capable, users are more likely to stop verifying sources and "ship" the first output the AI provides.

Takeaways

• Sentiment: While these tools are dominant, their success creates a "quality debt" within organizations.
• Actionable Insight: For the general public, the "High AI Achiever" profile suggests reinvesting time saved by these tools into new skill acquisition rather than just increasing the volume of AI-generated output.

Enterprise AI Adoption Metrics

The podcast provides specific data points for evaluating how well a company is integrating AI.

• The Multiplier Effect:
  • Leader using AI = 2.4x more likely for employees to adopt.
  • Direct teammate using AI = 3.2x more likely.
  • Cross-functional teammate using AI = 5.6x more likely.
• Governance: 93% of "transformative" organizations review their AI policy regularly, compared to only 55% of laggards.

Takeaways

• Risk Factor: "Tool Sprawl" is a significant drag on productivity. Organizations using too many disconnected AI tools face higher employee burnout and lower ROI.
• Management Insight: The best managers are delegating "coordination work" (status updates, meeting summaries) to AI to focus on "human work" (coaching and development). Companies following this model are likely to have higher employee retention.